Metabonomic Methods to Assess Health of Skin

ABSTRACT

The present invention relates to methods of assessing the health of skin. Biomarkers are used to evaluate skin samples. Using metabonomics approaches, samples taken from different skin sites or at different times during a treatment are used to diagnose skin conditions or to appraise various skin treatments for efficacy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/707,670, filed on Feb. 16, 2006, which claims the benefit of U.S.application Ser. No. 11/362,627, filed on Feb. 27, 2006, each of whichis herein incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to methods of assessing the health ofskin. More particularly, the present invention provides methods forevaluating skin using measurements of metabolites from skin samples. Thepresent invention further provides methods for assessing the efficacy oftreatments for skin conditions.

BACKGROUND OF THE INVENTION

Clinical and non-clinical scientific studies of biological responses,and in particular, dermatological responses, are hindered when currentmethods or devices cannot detect weak responses or distinguish amongmultiple causes for an observed response. Such studies are furtherhindered when the response being studied occurs with relatively low andunpredictable frequency among the general population.

Spectroscopic data of metabolites from biological samples are complex,and visual inspection can only yield a small amount of the informationavailable. A different technique, coined metabonomics by JeremyNicholson and mirrored after genomics and proteomics, has been developedto extract the maximum information from the complex spectra measured.Specifically, metabonomics is a quantitative measurement of the dynamicmultiparametric metabolic response of living systems topathophysiological stimuli or genetic modification (Lindon et al., Prog.NMR Spectrosc., 39:1 (2001)). What makes metabonomics potentially morepowerful than either genomics or proteomics is that many disease statesinvolve more than one gene or protein, but involve a finite number ofmetabolites. By studying spectroscopic data of small moleculemetabolites from a biological sample, one can trace the levels of themetabolites of interest during the course of a disease state incomparison to a control sample. The dynamic and time-dependent profilesof the metabolites allow for the appraisal of treatments and, in somecases, can assist in diagnosis of a disease state.

One particularly useful means of measuring metabolites in a biologicalsample is nuclear magnetic resonance (NMR). High field proton NMRspectra have been measured and compared for metabolite level differencesbetween diseased subjects and control subjects, as well as forhistorical analyses of metabolite levels over a period of time for adisease. The spectra have been amassed in a database and can be used forcomparison with future samples. Most applications of metabonomics havefocused on blood and urine samples. There is now a large database ofspectra of both urine and blood from subjects diagnosed with a widerange of diseases, such as the proprietary databases of Metabomatrix(London, UK), or annual reports such as, e.g., “Annual Reports on NMRSPECTROSCOPY” G. A. Webb, ed., Academic Press, volume 38: 1-88 (1999).

Little work has been done on the metabolites involved in skin conditionsor on how those metabolites change during the course of a diseased orchallenged state. Thus, there exists a need in the art to more clearlyelucidate metabolites associated with skin conditions, and changestherein, and to provide methods of assessing efficacy of skintreatments. Such methods would be particularly useful where enhancing orameliorating a response to a challenge is desirable. Methods of thistype would also be useful when the response of interest needs to bestudied under both highly controlled and poorly-controlled challengescenarios.

SUMMARY OF THE INVENTION

The present invention allows for the assessment of skin conditions in aquantitative manner. The invention provides methods for assessingchanges in skin conditions, comprising comparing biomarkers onchallenged skin to biomarkers on control skin, wherein the comparisonbetween the biomarkers indicates a change in skin condition. Theinvention further provides methods of assessing a change in skincondition, comprising isolating biomarkers from challenged skin andbiomarkers from control skin, and comparing individual analyses ofbiomarkers from challenged skin to biomarkers from control skin. Incertain cases, the biomarkers of challenged skin are monitored throughtime and/or across treatments as a means of assessing recovery of skinand/or efficacy of treatment. In certain cases, one of the biomarkersanalyzed is urocanic acid. In one embodiment, the method of assessingbiomarkers is carried out using NMR, mass spectrometry, liquidchromatography, capillary electrophoresis, other chromatographictechniques and/or combinations thereof.

Another aspect of the invention provides methods to assess a change inskin condition, comprising removing biomarkers from challenged skin andbiomarkers from control skin with separate adhesive strips, extractingfrom the adhesive strips biomarkers from challenged skin and biomarkersfrom control skin, analyzing extracted biomarkers from challenged skinand biomarkers from control skin, and comparing results from theanalysis, wherein the difference between the biomarkers of thechallenged skin and the biomarkers of the control skin indicate a changein skin condition. In certain cases, one of the biomarkers analyzed isurocanic acid. In one embodiment, the analysis is performed using NMR,mass spectrometry, liquid chromatography, capillary electrophoresis,other chromatographic techniques and/or combinations thereof.

Another embodiment of the present invention provides methods to assess achange in skin condition wherein the skin condition is associated with atopical challenge, a therapeutic challenge, a prophylactic challenge, ora pathological challenge. In one aspect, the topical challenge isocclusion. Contemplated occlusions include without limitation thoseresulting from contact with clothing, injury dressing, diaper, adultincontinence products, feminine hygiene products, and/or a substance.Substances include human substances or foreign substances. Therapeutic,cosmetic, or prophylactic challenges contemplated include topicalmedicaments to alleviate or prevent skin diseases or disorders orcleansers for the skin. Topical medicaments include solutions, wipes,ointments, powders, creams, or lotions. Pathological challengescontemplated include bacterial infections, viral infections, andparasitic infections.

Another aspect of the present invention provides arrays of data whichmay be used as predictive models of the state of health of skin of anindividual sample. These arrays of data may be assembled from analysesof skin samples of known skin health. The set of biomarkers of thesample of unknown skin health is analyzed and compared to the array ofdata for similarity to sets of biomarkers of samples of known skinhealth in order to predict the state of health of the unknown sample.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A shows a 700 MHz ¹H NMR spectrum of an aqueous extraction of tapestrip collected from occluded adult forearm skin. Spectrum correspondsto proton chemical shift range of about 0.5 to 2.5 ppm. All chemicalshifts are reported relative to TMS.

FIG. 1B shows ¹H NMR spectrum per FIG. 1A showing chemical shift rangeof about 2.5 to 4.5 ppm.

FIG. 1C shows ¹H NMR spectrum per FIG. 1A showing chemical shift rangeof about 5.5 to 8.5 ppm.

FIG. 2A shows 600 MHz ¹H NMR spectra of extraction of tape stripscollected from adult forearm skin following full occlusion for 72 hours(A), partial occlusion for 72 hours (B), non-occluded control (C). Waterresonance (about 4.8 ppm) is excluded.

FIG. 2B shows expanded aliphatic regions corresponding to ¹H NMR spectrain FIG. 2A.

FIG. 2C shows expanded aromatic regions corresponding to ¹H NMR spectrain FIG. 2A.

FIG. 3 shows 600 MHz ¹H NMR spectra (aromatic region) of tape stripextractions of adult forearm skin (A) and with cis- and trans-urocanicacid spike (B). Chemical structure of trans-urocanic acid shown asinsert.

FIG. 4A shows 600 MHz ¹H NMR spectra (aliphatic region) of tape stripextractions of adult forearm skin (A) and with histidine spike (B).Chemical structure of histidine shown as insert.

FIG. 4B shows 600 MHz ¹H NMR spectra (aromatic region) of tape stripextractions of adult forearm skin (A) and with histidine spike (B).Chemical structure of histidine shown as insert.

FIG. 5 shows 600 MHz ¹H NMR spectra (aliphatic region) of tape stripextractions of adult forearm skin (A) and with lactic acid spike (B).Chemical structure of lactic acid shown as insert.

FIG. 6 shows a scores plot for skin samples grouped into occluded andcontrol.

FIG. 7A shows a diagram identifying various test sites of baby skin.

FIG. 7B shows 600 MHz ¹H NMR spectra (aliphatic region) of aqueousextractions of tape strips from the various sites as identified in FIG.7A.

FIG. 7C shows 600 MHz ¹H NMR spectra (aromatic region) of aqueousextractions of tape strips from the various sites as identified in FIG.7A.

FIG. 8 shows 500 MHz ¹H NMR spectra (aromatic region) of tape skinextractions of adult forearm occluded skin (A), after one wipe with aZnO lotion wipe (B), and after five wipes with ZnO lotion wipe (C).

FIG. 9 shows a three-dimensional scores plot of skin in differentstates: untreated normal, occluded and wipe-cleaned.

FIG. 10 shows a spatial separation of three skin conditions (untreatednormal, occluded, and wipe-cleaned) on a three-dimensional scores plotfrom a subject.

FIG. 11 shows 500 MHz ¹H NMR spectra (aromatic region) of tape skinextractions of adult forearm occluded skin (A), skin wiped with ZnOlotion wipe (B), and skin wiped with vehicle (water/preservative only)lotion wipe (C).

FIG. 12 shows separation of ZnO lotion wipes treated skin from vehicle(water/preservative only) wipes treated skin and occluded control skinon a two-dimensional scores plot.

FIG. 13A shows a three-dimensional scores plot of various skin samples,with outlier subjects separated.

FIG. 13B shows an expanded plot of the three-dimensional scores plot ofFIG. 13A and is shown without subjects #113 and #115.

DETAILED DESCRIPTION OF THE INVENTION

Metabonomics approaches have previously been evaluated for biofluidssuch as blood and urine, and in biological samples such as tissues andtumors. Methods of the invention use metabonomics to analyze challengedskin samples. Comparison of data generated from challenged skin versusdata from control, e.g., normal, healthy skin with respect to biomarkerson the skin surface, both from the same subject (to measure skin healthover time or to compare treatment efficacies) or among differentsubjects, may allow for a better understanding of the means of skinchallenge and the effectiveness of various skin treatments.

As used herein, “challenge” refers to the entity or process ofprovoking, moderating or otherwise influencing a physiological(including biochemical) activity by exposure under defined conditions toone or more physical, chemical or biological condition or action. Insome cases, the challenge is a prophylactic or therapeutic challenge.Nonlimiting examples of such prophylactic or therapeutic challengesinclude topical medicaments and cleaners. Topical medicaments andcleaners include solutions, wipes, ointments, powders, creams, andlotions. These medicaments or cleansers can be of a type to alleviate orprevent skin disorders or conditions.

As used herein, “control skin” or “control” refers to any test site orstudy treatment used to establish a standard of comparison for judgingexperimental effects and/or the degree of variation in effects thatoccur during a study. Such controls include but are not limited tounchallenged skin, skin undergoing an alternate treatment, skinchallenged by a different challenge, and the like.

As used herein, “biomarkers” are metabolites that are recoverable fromskin and may be involved in the skin challenges of interest. Thesebiomarkers may be identified in the spectral assessment of the samples,or they may be unknown but tracked over time or over a series of samplesites, to assess their presence and role in the conditions of interest.Unknown biomarkers that are determined to play a role in a resultingskin condition can be later identified using techniques well-known androutinely practiced in the art. Biomarkers, or metabolites, may beidentified using the proprietary databases, such as those ofMetabomatrix (London, UK), or comparing spectra to those published inannual reports such as, e.g., “Annual Reports on NMR SPECTROSCOPY” G. A.Webb, ed., Academic Press, volume 38: 1-88 (1999).

One complicating factor in assessing the state of skin is that there isa lack of homogeneity of skin samples due to the variation of skin typefrom different body locations, life style, diet, age, and evendemographic differences. Individual differences have previously causeddifficulties in predicting outcomes of clinical studies which rely onlyon visual grading and non-specific measurements such as Trans-epidermalwater loss (TEWL). Further, skin samples are subject to a variety ofenvironmental contaminations that can complicate analysis. In trying toremove these environmental contaminants with conventional approaches,one can alter the levels of metabolites in the skin sample and skew thespectroscopic profile of the sample. The metabonomic approach, as aholistic approach for metabolite analysis, may be able to isolate thedifferences and even identify the metabolites that are responsible forthe individual variations.

Metabonomic assessment can be performed on one subject for comparison ofmetabolite levels in different skin sites or at different skin depths,or among different subjects with different skin types or otherindividual factors. These assessments bring insight into the way thatmetabolites at different skin sites or different skin types can point tocertain skin challenges, and indicate the effectiveness of a skintreatment for one skin type or one skin challenge against a differentone. Control of the skin environment is one way to ensure that samplestaken from subjects are a true assessment of the skin metabolites due toparticular challenges.

Factors that can affect the biomarkers detected from samples includeskin challenges, skin microflora, metabolism over time, depth of skinsampled, and the like. In certain instances, the level of a metabolitemay increase as the length of time between cleaning increases. Thistrend may be opposite to a metabolite gradient that decreases in skindepth according to depth profiling by ten consecutive samples taken atthe same site. Thus, samples taken from a subject with challenged skinmay show a different profile of metabolites than that of the samesubject after treatment such as water washing or with a skin cleaningproduct. In this respect, the metabonomic approach provides anunderstanding of the impact on the methods available for substantiatinga particular product's effectiveness versus that of a competing product.

Skin challenges of interest in the methods defined herein can be of aphysical, chemical, or biological nature. Examples of physicalchallenges include, but are not limited to heat, cold, humidity,desiccation, radiant energy, dark, friction, abrasion, lubrication,electric or magnetic energy, pressure, vacuum, visual images such astattoos, occlusion, and sound. Physical challenges include those thatcan arise from direct or indirect contact with an individual's skinwith, including for example and without limitation diapers, condoms,injury dressings, feminine care products, bandages, clothing, acute orprolonged compression, trauma and/or abrasion, and the like. In somecases, physical challenges occur from reduced breathability of the skindue to these contacts. Some examples of chemical challenges include anyelement of the periodic table or compositions of those elementsincluding, but not limited to organic and inorganic acids and bases orconditions caused by their presence in aqueous and/or non-aqueoussystems, surfactants, metals and complexes thereof, salts, proteins,lipids, fats, carbohydrates, vitamins, polymers, pharmaceuticals, feces,urine, perspiration, hair, blood, nasal discharge, saliva, tears, earwax, extracts, and enzyme digests. Some examples of biologicalchallenges include, but are not limited to microbials, cells, viruses,bacteria, fungi, parasites, and/or other living entities includingeukaryotes which directly induce a range of responses and/or indirectlyare responsible for responses when they are vectors for the transmissionof biological challenges. Biological challenges also include those whicharise endogenously, including conditions such as from metabolic,genetic, dietary, and/or autoimmune disorders. Any challenges to thebody that causes a skin change in biomarker presentation can beevaluated using methods of the present invention. Thus, the methods ofthe invention may be powerful tools in screening potential new productsfor their breathability and skin benefits. These methods may indicatepromising uses for evaluating product efficacy. In addition, theseapproaches can be utilized in appropriate clinical study designs toassess the kinetics of skin recovery or for evaluating a person's healthstatus, e.g. a prematurely born baby. The methods may allow formetabolites responsible for the separation of skin treatments orconditions to be identified in further analysis.

Methods of the invention are useful for a variety of assessments of skinconditions. In one aspect, severity of a skin condition can be evaluatedbased on a change, either an increase or decrease, in the level of oneor more specific biomarkers associated with a specific challenge. Incertain instances, severity of a challenge can also be assessed byidentifying a change in a biomarker expression pattern known to follow aspecific challenge. For example, a specific challenge may give rise to aspecific biomarker expression pattern that is replaced, in one or morephases, with one or more specific expression patterns as the resultingcondition deteriorates.

In another aspect, an improvement in skin condition can be assessedwherein a specific biomarker expression pattern associated with aspecific challenge is first identified and subsequently observed to movecloser to the biomarker expression pattern observed to be associatedwith control, or normal skin. Such an assessment may simply follow achange in biomarker presentation following removal of the challenge ormay follow therapeutic (after challenge) or prophylactic (prior tochallenge) intervention. Accordingly, assessment of the efficacy ofparticular skin treatments is one aspect of the present invention. Incertain cases, assessment of the efficacy of treatment or the state ofhealth of skin involves comparing time point samples from the samesubject.

In methods of the invention, any of a number of techniques for obtaininga sample of skin biomarkers can be employed. In various aspects,mechanical scraping, swabbing and/or direct elution, pressure blotting,electric transfer, or the like may be employed. In another aspect,“tape-stripping” is utilized. As used herein, “tape-stripping” refers tothe act of applying a material of known adhesive properties to skin in aprescribed manner and removing that material for the purposes ofsubjecting the skin to a physical challenge. Adhesive strips useful fortape-stripping, which need not specifically be in a tape format, includeadhesive tapes such as D-squame™ and Sebutape™ (CuDerm Corporation,Dallas, Tex.) or Blenderm™ and ScotchTape™ (3M Company, St. Paul,Minn.), and hydrogels such as Hypan™ (Hymedix International, Inc.,Dayton, N.J.), and other types of materials with adhesive propertiessuch as glues, gums, and resins.

Regardless of the method used for obtaining a sample of skin biomarkerspresented on the skin to be analyzed, in most instances the biomarkersare removed from the device or liquid used to obtain the sample andprocessed in such a manner that allows for assessment of the biomarkers.In general, the chosen method of analysis will dictate how the biomarkersample is processed, such processing techniques being well known in theart.

Spectral measurements are best suited for metabonomics when they allowfor the separation of signals due to different metabolites. Highresolution NMR is one means for measuring small molecule metabolites ina biological sample. Other means for measurement include massspectrometry (MS), capillary electrophoresis (CE), liquid chromatography(LC), and combinations thereof. All of the preceding analytical methodshave some method of extricating different metabolite data points. Use ofNMR for metabonomics permits a wide range of metabolites to bequantified simultaneously with simple sample preparation. Additionally,NMR is powerful and sensitive enough to detect sub-clinical skinchanges, i.e., before skin changes become visible, at a molecular levelin a non-subjective/non-biased fashion from tape strips. For example,the NMR approach may provide a tool to measure skin impact of occlusionand wipes cleaning effects quantitatively. In addition, this approachcould be used for monitoring metabolite recovery process in skin, animportant parameter of skin benefits. Accordingly, the NMR approach foranalyzing skin benefits can be used for product skin benefitsevaluation: (1) by measuring occlusion effect for breath-ability ofdiaper, (2) by determining wipe cleaning effect for lotion wipes urineand BM cleaning efficacy, and (3) by monitoring the process of skinmetabolites shifting back to normal population after wipe cleaning todemonstrate skin health benefit. Monitoring metabolite recovery processin skin after cleaning can also be a useful approach for demonstratingskin benefits of many beauty care products and may also predict skinhealth status such as for prematurely born babies whose skin function iscritical for their survival.

NMR spectroscopy is based on the behavior of atoms placed in a staticexternal magnetic field. Atomic nuclei possessing a property known asspin that is not equal to zero can give rise to NMR signals. Nucleipossessing this property include ¹H, ¹³C, ¹⁵N and ³¹P. Since protons arepresent in almost all metabolites in body fluids, an ¹H NMR spectrumallows the simultaneous detection and quantification of thousands ofproton-containing, low-molecular weight species within a biologicalmatrix, resulting in the generation of an endogenous profile that may bealtered in disease to provide a characteristic “fingerprint” (Nicholson,J. K., Lindon, J. C., and Holmes, E. Xenobiotica, 29: 1181-1189 (1999);Lindon, J. C., Nicholson, J. K., and Everett, J. R. Annu. Rep. NMRSpectrosc., 38: 1-88 (1999); Lindon, J. C., Nicholson, J. K., Holmes,E., and Everett, J. R., Concepts Magn. Reson., 12: 289-320 (2000); andNicholson, J. K., Connelly, J., Lindon, J. C., and Holmes, E. Nat. Rev.Drug Discov., 1: 153-161, (2002)). A range of NMR strategies has alsobeen developed for structure elucidation of metabolites in biofluids.

Pattern Recognition (PR) is essential for NMR spectra of biologicsamples because the samples are extremely complex, and much informationcan be lost even in rigorous statistical analysis of quantitative dataas the essential diagnostic parameters are carried in the overallpatterns of the spectra. Therefore, in order to reduce NMR datacomplexity and facilitate analysis, data-reduction followed bychemometric methods, such as Principal Components Analysis (PCA) andPartial Least Squares-Discriminant Analysis (PLS-DA), can be applied.Various other treatments of the data can be used as appropriate, and canbe easily determined by one of skill in the art.

The intrinsic accuracy of NMR provides a distinct advantage whenapplying pattern recognition techniques. The multivariate nature of theNMR data means that classification of samples is possible using acombination of descriptors even when one descriptor is not sufficient,because of the inherently low analytical variation in the data. Allbiological fluids and tissues have their own characteristicphysico-chemical properties, and these affect the types of NMRexperiment that may be usefully employed. One major advantage of usingNMR spectroscopy to study complex biomixtures is that measurements canoften be made with minimal sample preparation and a detailed analyticalprofile can be obtained on the whole biological sample. Sample volumesare small, typically 0.3 to 0.5 mL for standard probes, and as low as 3μL for microprobes. Acquisition of simple NMR spectra is rapid andefficient using flow-injection technology. It is usually necessary tosuppress the water NMR resonance.

In all cases the analytical problem usually involves the detection of“trace” amounts of analytes in a very complex matrix of potentialinterferences. It is, therefore, critical to choose a suitableanalytical technique for the particular class of analyte of interest inthe particular sample. High resolution NMR spectroscopy (in particular¹H NMR) was found to be particularly appropriate. The main advantages ofusing ¹H NMR spectroscopy are the speed, the requirement for minimalsample preparation, and the fact that it provides a non-selectivedetector for all metabolites in the sample regardless of theirstructural type, provided only that they are present above the detectionlimit of the NMR experiment and that they contain non-exchangeablehydrogen atoms.

In one aspect, NMR studies of samples are performed at the highestmagnetic field available to obtain maximal dispersion and sensitivity.In one embodiment, ¹H NMR studies are performed at 500 MHz or greater.With every new increase in available spectrometer frequency the numberof resonances that can be resolved in a sample increases, and althoughthese increases have the effect of solving some assignment problems,they also pose new ones. Furthermore, there are still important problemsof spectral interpretation that arise due to compartmentation andbinding of small molecules in the organized macromolecular domains thatexist in some samples. All of this complexity need not reduce thediagnostic capabilities and potential of the technique, but demonstratesthe problems of biological variation and the influence of variation ondiagnostic certainty.

The information content of the sample spectra is very high and thecomplete assignment of the ¹H NMR spectrum is not always possible (evenusing 900 MHz NMR spectroscopy). However, the assignment problems varybetween sample types. Those metabolites present close to the limits ofdetection for 1-dimensional (1D) NMR spectroscopy (typically about 100nM at 800 MHz) pose NMR spectral assignment problems. In absolute terms,the detection limit may be about 4 nmol, e.g., 1 μg of a 250 g/molcompound in a 0.5 mL sample volume. Even at the present level oftechnology in NMR, it is not yet possible to detect many importantbiochemical substances (e.g. hormones, some proteins, nucleic acids) ina sample because of problems with sensitivity, line widths, dispersionand dynamic range and this area of research will continue to betechnology-limited. In addition, the collection of NMR spectra ofbiological samples may be complicated by the relative water intensity,sample viscosity, protein content, lipid content, and low molecularweight peak overlap.

Usually in order to assign ¹H NMR spectra, comparison is made withspectra of authentic materials and/or by standard addition of anauthentic reference standard to the sample. Additional confirmation ofassignments is usually sought from the application of other NMR methods,including, for example, 2-dimensional (2D) NMR methods, particularlyCOSY (correlation spectroscopy), TOCSY (total correlation spectroscopy),inverse-detected heteronuclear correlation methods such as HMBC(heteronuclear multiple bond correlation), HSQC (heteronuclear singlequantum coherence), and HMQC (heteronuclear multiple quantum coherence),2D J-resolved (JRES) methods, spin-echo methods, relaxation editing,diffusion editing (e.g., both 1D NMR and 2D NMR such as diffusion-editedTOCSY), and multiple quantum filtering.

All of these factors contribute to the importance of using statisticalanalysis to assess the NMR data and reduce the complexity of the datasets. In general, the use of PR algorithms allows the identification,and, with some methods, the interpretation of some non-random behaviorin a complex system which can be obscured by noise or random variationsin the parameters defining the system. Also, the number of parametersused can be very large such that visualization of the regularities,which for the human brain is best in no more than three dimensions, canbe difficult. Usually the number of measured descriptors is much greaterthan three and so simple scatter plots cannot be used to visualize anysimilarity between samples. In the context of the methods describedherein, PR is the use of multivariate statistics, both parametric andnon-parametric, to analyze spectroscopic data, and hence to classifysamples and to predict the value of some dependent variable based on arange of observed measurements. There are two main approaches. One setof methods is termed “unsupervised” and these simply reduce datacomplexity in a rational way and also produce display plots which can beinterpreted by the human eye. The other approach is termed “supervised”whereby a training set of samples with known class or outcome is used toproduce a mathematical model and this is then evaluated with independentvalidation data sets.

Unsupervised PR methods are used to analyze data without reference toany other independent knowledge, for example, without regard to theidentity or nature of a xenobiotic or its mode of action. Examples ofunsupervised pattern recognition methods include principal componentanalysis (PCA), hierarchical cluster analysis (HCA), and non-linearmapping (NLM).

One useful and easily applied unsupervised PR technique is principalcomponents analysis (PCA). Principal components (PCs) are new variablescreated from linear combinations of the starting variables withappropriate weighting coefficients. The properties of these PCs are suchthat (i) each PC is orthogonal to (uncorrelated with) all other PCs, and(ii) the first PC contains the largest part of the variance of the dataset (information content) with subsequent PCs containing correspondinglysmaller amounts of variance.

PCA, a dimension reduction technique, takes m objects or samples, eachdescribed by values in k dimensions (descriptor vectors), and extracts aset of eigenvectors, which are linear combinations of the descriptorvectors. The eigenvectors and eigenvalues are obtained bydiagonalization of the covariance matrix of the data. The eigenvectorscan be thought of as a new set of orthogonal plotting axes, calledprincipal components (PCs). The extraction of the systematic variationsin the data is accomplished by projection and modeling of variance andcovariance structure of the data matrix. The primary axis is a singleeigenvector describing the largest variation in the data and is termedprincipal component one (PC1). Subsequent PCs, ranked by decreasingeigenvalue, describe successively less variability. The variation in thedata that has not been described by the PCs is called residual varianceand signifies how well the model fits the data. The projections of thedescriptor vectors onto the PCs are defined as scores, which reveal therelationships between the samples or objects. In a graphicalrepresentation (a “scores plot” or eigenvector projection), objects orsamples having similar descriptor vectors will group together inclusters. Another graphical representation is called a loadings plot,which connects the PCs to the individual descriptor vectors and displaysboth the importance of each descriptor vector to the interpretation of aPC and the relationship among descriptor vectors in that PC. In fact, aloading value is simply the cosine of the angle which the originaldescriptor vector makes with the PC. Descriptor vectors which fall closeto the origin in this plot carry little information in the PC, whiledescriptor vectors distant from the origin (high loading) are importantin interpretation.

Thus a plot of the first two or three PC scores gives the “best”representation, in terms of information content, of the data set in twoor three dimensions, respectively. A plot of the first two principalcomponent scores, PC1 and PC2 provides the maximum information contentof the data in two dimensions. Such PC maps can be used to visualizeinherent clustering behavior, for example, for drugs and toxins based onsimilarity of their metabonomic responses and hence mechanism of action.Of course, the clustering information might be in lower PCs and thesehave to be examined also. The PC loadings where each point representsone variable are used to detect those variables, or spectral readings inthe case of a complex NMR spectrum, responsible for any separation ofsamples into clusters. After PCA of NMR spectral data, the moleculesresponsible for any separation in the data can then be characterizedusing 2-D NMR analysis or hyphenated separation techniques, such as LC,MS or CE (LC-NMR, MS-NMR, or CE-NMR).

Hierarchical Cluster Analysis, another unsupervised pattern recognitionmethod, permits the grouping of data points which are similar by virtueof being “near” one another in some multidimensional space. Individualdata points may be, for example, the signal intensities for particularassigned peaks in an NMR spectrum. A “similarity matrix,” S, isconstructed with elements s_(ij)=1−r_(ij)/r_(ij) ^(max), where r_(ij) isthe interpoint distance between points i and j (e.g., Euclideaninterpoint distance), and r_(ij) ^(max) is the largest interpointdistance for all points. The most distant pair of points will haves_(ij) equal to 0, since r_(ij) ^(max) then equals r_(ij) ^(max).Conversely, the closest pair of points will have the largest s_(ij). Fortwo identical points, s_(ij) is 1.

The similarity matrix is scanned for the closest pair of points. Thepair of points are reported with their separation distance, and then thetwo points are deleted and replaced with a single combined point. Theprocess is then repeated iteratively until only one point remains. Anumber of different methods may be used to determine how two clusterswill be joined, including the nearest neighbor method (also known as thesingle link method), the furthest neighbor method, and the centroidmethod (including centroid link, incremental link, median link, groupaverage link, and flexible link variations).

The reported connectivities are then plotted as a dendrogram (a treelikechart which allows visualization of clustering), showing sample-sampleconnectivities versus increasing separation distance (or equivalently,versus decreasing similarity). The dendrogram has the property in whichthe branch lengths are proportional to the distances between the variousclusters and hence the length of the branches linking one sample to thenext is a measure of their similarity. In this way, similar data pointsmay be identified algorithmically.

Non-linear mapping (NLM) is a simple concept which involves calculationof the distances between all of the points in the original k dimensions.This is followed by construction of a map of points in 2 or 3 dimensionswhere the sample points are placed in random positions or at valuesdetermined by a prior principal components analysis. The least squarescriterion is used to move the sample points in the lower dimension mapto fit the inter-point distances in the lower dimension space to thosein the k dimensional space. Non-linear mapping is therefore anapproximation to the true inter-point distances, but points close ink-dimensional space should also be close in 2 or 3 dimensional space.

The statistical analysis may comprise multivariate analysis, inparticular by a partial least squares (PLS) method on the group ofcontrol samples. This produces a calibration data set, e.g. a set ofmetabolite scores or other factor scores from the control data. The PLSmethod makes it possible to establish a regression model between atleast one estimated variable said to be dependent or latent, andvariables that are said to be independent or manifest and that explainthe variations in the latent variable.

“Supervised” PR methods may be appropriate when class membership isknown for a set of observations. For such data sets, PCA uncovers thedirections in multivariate space that represent the largest sources ofvariation. The maximum variation defined by these PCs does notnecessarily coincide with the maximum separation directions among theclasses. Instead, it may be that other directions are more pertinent fordiscriminating among classes of observations. Partial least squaresdiscriminant analysis (PLS-DA) makes it possible to accomplish arotation of the projection to give latent variables that focus on classseparation. This method offers a convenient way to explicitly take intoaccount the class membership of observations. Thus, PLS-DA develops amodel that separates classes of observations on basis of their originalx-variables. This model is based on a training set of observations withknown class membership.

Molecular Factor Analysis (MFA) is a set of NMR data processing toolsdeveloped to identify and quantify the components of complex mixturesbased on NMR spectra. The input is a set of NMR spectra of the mixtures.First, the data is reduced using singular value decomposition. Theresults are a set of principal component eigenspectra that containenough information to reconstruct component spectra. However, themathematical analysis that leads to principal components does not takeinto consideration the physical or chemical knowledge about the natureof NMR spectra, such as, e.g., all peaks in an NMR spectrum must bepositive, all concentrations must be positive, and Beer's law must beobeyed. MFA algorithms allow one to find n different combinations of then principal component eigenspectra that conform to all these facts so asto extract meaningful spectroscopic information from large NMR datasets. (Eads et al., Analytical Chem 76(7): 1982-1990 (2004))

The methods described herein, which employ pattern recognitiontechniques, permit identification of that NMR peak intensity which isrelated to the condition under study, even though only a small part ofthe variance in a spectral region may be related to the condition understudy. The identification power is enhanced by the application of datafiltering techniques (e.g., orthogonal signal correction, OSC) which canlower the influence of regions with variance unrelated to the conditionof interest. Actual identification of the molecular biomarkerscontributing to significant regions is carried out by reexamination ofthe original NMR spectra and could involve additional NMR spectroscopicexperiments such as 2-dimensional NMR spectroscopy; separation ofputative substances and their identification using HPLC-NMR-MS or otheranalytical combination techniques; addition of authentic substance tothe sample and re-measuring the NMR spectrum, checking for coincidenceof NMR peaks, etc.

Furthermore, the methods can be applied to achieve classification intomultiple categories on the basis of a single dataset, for example, anNMR spectrum for a single sample. Due to the very high data density ofthe input dataset, the analysis method can separately (i.e., inparallel) or sequentially (i.e., in series) perform multipleclassifications. For example, a single skin sample could be used todetermine (e.g., diagnose) the presence or absence of several, orindeed, many, conditions or diseases.

Appropriate PR techniques can generate models from a well definedtraining set. These models can then be used to classify unknown sampleswith a single ¹H NMR spectrum. For example, a single skin sample couldbe used to determine (e.g., diagnose) the presence or absence of acertain skin condition or disease. In one aspect, therefore, an assemblyof data is provided as a predictive means for assessing the state ofskin of individual samples. This assembly is generated by the collectionof skin biomarker samples in a variety of skin states (e.g., healthy,occluded, soiled, and cleaned). Biomarkers in each skin sample aremeasured using an analytical technique, such as NMR, HPLC, massspectrometry, and the like, to determine the presence (or absence) ofbiomarkers, and PR techniques are employed to generate scores of variousfactors, or biomarkers, for each sample. An array of scores forindividual biomarkers can be assembled from the score analyses of theskin samples. Because the skin biomarker samples are from skin of aknown state (e.g., healthy, occluded, soiled, and cleaned), thecombination of scores can be tracked to identify partitioned sections ofthe array which are indicative of individual skin states. For example,FIG. 9 shows a 3D scores plot and the grouping of skin samples by skinstate. Once the partitions of the array are identified, the array can beused as a predictive tool for assessing individual skin samples ofunknown condition. Analysis of biomarkers in any skin sample to createscores allows for the skin sample to be “plotted” into this array andpredict its skin state. In some embodiments, the array may be furtherpartitioned within a skin state to specific, e.g., diseases, challenges,or treatments. This comparison to the arrayed data, or model, allows forthe diagnosis of a skin challenge, comparison of different treatmentsfor a challenge, assessment of the effect of a contact challenge on thestate of skin, and other types of comparisons between skin samples andconditions.

Additional aspects and details of the invention will be apparent fromthe following examples, which are intended to be illustrative ratherthan limiting.

EXAMPLES Example 1 Normal vs. Occluded Skin

Occlusion induced skin changes may not be easily or reproduciblyevaluated by visual grading. Furthermore, the visual grading will notprovide critical information needed in order to understand and predictskin responses to certain treatments and to create measures to maintainor promote skin health. Sensitive techniques with high resolving powerare needed to analyze skin at the molecular level.

Skin samples are collected from a clinical study that is carried out onadult arms. Each arm has two of the three sites assigned to treatmentwith either a fully occlusive patch (Saran Wrap®) or a patch preparedfrom a more breathable material, such as a Hytrel film (Dupont), with alow moisture vapor transmission rate (MVTR) of about 350 g/m²/24 hours.The third site is a non-occluded control. The area of the skin where apatch will be placed is marked. The treatment patch is placed on theskin and secured to the skin with Tegaderm tape (3M). Each patch site ispatched for approximately 24 hrs with a 4×5 cm² patch made of one of thetest materials. Some sites are repatched for 2 additional 24 hr periods.Skin samples are collected by D-Squame™ tape stripping and stored at orbelow −20° C. until use. Each skin tape is extracted by placing a singletape in a glass vial and adding 200 μL D₂O. The tissue side of the stripshould be face down in the vial for good contact with the solvent. Thevial is sonicated for 15-30 min. and vortexed for 30 sec. The extractsare transferred to sample tubes for ¹H NMR measurement.

The 700 MHz ¹H NMR spectra of an aqueous extraction of a tape stripcollected from occluded adult forearm skin are shown in FIG. 1A, FIG. 1Band FIG. 1C along with ¹H resonance assignments. The assignments arebased on a combination of spiking experiments and literature referencesas noted earlier. They are: 1 alpha-OH-n-butyrate; 2 lipid (—CH₃); 3isoleucine; 4 leucine; 5 valine; 6 isoleucine; 7 valine; 8 lactate; 9alanine; 10 leucine; 11 lysine, 12 lysine; 13 acetate; 14 proline; 15glycoprotein; 16 proline; 17 glutamine; 18 acetoacetate; 19 valine; 20glutamate; 21 glutamine; 22 glutamate; 23 N—CH3; 24 CH3 next to NH; 25N—CH3; 26 lysine; 27 citrulline; 28 phenylalanine; 29 proline; 30glycine; 31 threonine; 32 valine; 33 alpha-H from amino acids; 34alanine; 35 serine; 36 hippurate; 37 histidine; 38 phenylalanine; 39lactate; 40 beta-OH-butyrate; 41 threonine; 42 urocanate; 43trans-aconitate; 44 tyrosine; 45 histidine; 46 tyrosine; 47 urocanate;48 phenylalanine; 49 phenylalanine; 50 urocanate; 51 phenylalanine; 52hippurate; 53 tryptophan; 54 tryptophan; 55 urocanate; 56 histidine; 57formate.

FIG. 2A shows the spectra of skin samples taken from a subject after 72hour occlusion. The spectra are acquired on a Varian INOVA-500. Becauseof the small sample volume, a 3 mm indirect detection probe is used.FIG. 2B shows the aliphatic regions of the spectra of FIG. 2A. FIG. 2Cshows the aromatic regions of the spectra of FIG. 2A. NMR spectra ofuntreated and occluded with partial breathable (partial occlusion) andnon breathable materials (full occlusion) in FIG. 2A, FIG. 2B and FIG.2C may show the changes both in aliphatic and aromatic region (bottomtrace—non-occluded control, the middle trace—partial occluded and uppertrace—full occluded). With the occlusion protocol, occlusion inducedskin changes are easily detected.

Example 2 Identification of Skin Metabolites

Skin metabolites that respond to occlusion are identified using variousapproaches. Samples from occluded and control skin are collectedfollowing the same method described in Example 1.

The skin strips are extracted with water and then analyzed bycapLC-MS/MS (LCQ LC-MS/MS system (ThermoFinnigan) with 173AMicroblotter-Capillary LC system (Applied Biosystems) and a 0.5 mm×15 cmC18 capillary column (Perkin Elmer). Comparison of the MS data reveal aunique signal with m/z 139 whose intensity is elevated in occluded skin.Exact mass measurement of the peak indicates a molecular ion of m/z139.0518 with a possible molecular formula of C₆H₆N₂O₂. There areseveral possible structures with the same molecular formula and similarmass spectra.

Several known compounds are tested having these characteristics usingMS/MS, and urocanic acid is identified as the biomarker in the occludedskin. Urocanic acid (UCA) is a natural component in the stratum corneumand is a metabolite formed by one step enzymatic reaction (deamination)from histidine. Trans-UCA is a natural form in stratum corneum. It canbe converted to cis-UCA via UV irradiation or sun exposure. In order toconfirm the biomarker identity, two separate experiments may beperformed using capillary electrophoresis (CE).

First, a pure UCA sample from Sigma is run through CE and a single peakis observed. The same sample solution is then exposed to UV light forone hour and run through CE again. The single UCA peak splits into twopeaks. The original UCA peak becomes smaller while a new peak appearswith a shorter migration time. The migration time of both trans- andcis-UCA match that of the two peaks of the biomarkers in an occludedskin extract.

Second, a sample from occluded skin is analyzed by CE before and afterUV exposure. As expected, the ratio of the biomarker peaks changes afterUV exposure. The peak corresponding to trans-UCA becomes smaller whilethe other peak (cis-form) becomes larger. In a spiking test, the twopeaks of UCA isomers superimpose to those of the skin extract. The twopeaks found in CE of occluded skin are thus confirmed as cis- andtrans-UCA.

Confirmation of the metabolites involved with skin conditions is thenassessed, along with the possibility that monitoring levels ofmetabolites identified could lead to useful models that could predictunknown samples.

A fully occlusive patch (Saran Wrap®) is applied to forearm skinovernight. Skin samples by D-Squame™ tape stripping are taken from bothtreated and control (non-occluded) sites. The samples are extracted byplacing a single skin strip in a glass vial, adding 1 mL water andsonicating the vials for 30 min. The sample extracts are dried undernitrogen (N₂) and are reconstituted in 200 μL of 200 mM sodium phosphatecontaining 0.1% (w/v) sodium azide in D₂O at pH 7.4. The samples aretransferred to sample tubes for ¹H NMR analysis. A 2.5 mm probe is useddue to the small sample size. The ¹H NMR spectra are collected using a600 MHz Bruker Avance NMR spectrometer employing a routine preset pulsesequence. A known amount of UCA solution is spiked into the skin extractand analyzed via ¹H NMR again. The NMR spectra may show enhanced peakscorresponding to the spiked UCA and facilitate the identification of thecompound that varies as a function of occlusion (FIG. 3).

In addition to identification of cis-urocanic acid (cis-UCA) andtrans-urocanic acid (trans-UCA) (FIG. 3), histidine (FIG. 4A and FIG.4B), and lactate (FIG. 5) are also identified using the above approach.

Example 3 Sample Variation Effects on Metabolite Profiles

Metabolite variations among skin type, depth, site, and time fromwashing are assessed. Five different sites from an adult forearm arestripped once using D-Squame™ tapes one hour after washing. The samefive sites are stripped again six hours after the first tape stripping.An additional set of samples are tape stripped consecutively ten timesfrom a separate site for skin depth profile study. Skin depth samplesare also collected from another individual. The samples are extracted byplacing a single skin strip in a glass vial, adding 1 mL water andsonicating the vial for 30 min. The sample extracts are dried undernitrogen (N₂) and are reconstituted in 200 μL of 200 mM sodium phosphatecontaining 0.1% (w/v) sodium azide in D₂O at pH 7.4, and transferred toa 2 mm capillary tube capped with a Teflon™ fixture. The capillary tubeis inserted into a NMR micro sample tube assembly (5 mm tube with 3 mmstem, from NewEra (New Jersey)). Annular space is filled with D₂O toprovide locking during spectrometer setup for ¹H NMR. Metaboliteprofiles of the ten consecutive tape strips from a single site arecompared.

Results may suggest that within the same individual, there is nosignificant chemical compositional difference up to ten tape strips indepth. However, the level of the water-extractable metabolite componentsmay be gradually reduced as the tape stripping progresses in depth.Samples from individuals in this study may show similar gradientdistribution. To mitigate the variation, the optimal depth of skinsampling should be determined and used for sample to sample comparison.

Within the same individual, different sites on the forearm may not showsignificant differences in the metabolic composition when freshlycleaned up to one hour after shower. However, variation may becomeobservable over time after the first tape stripping, which may be aresult of physical activity and clothing, such as a site having the mostsignificant change may be close to the wrist. Control of skinenvironment may reduce the variation.

For example, the level of water-extractable metabolites may be higher inskin strips taken at six hours post washing than the first strips takenat one hour post washing. The increased level of metabolites in the sixhour strips may be observed in all five sites. This increase may be dueto a skin response to disrupted barrier by the first stripping or as aresult of natural accumulation of the metabolites in the skin during theday. There is no significant chemical composition change among thestrips taken from two different times.

Visual inspection of the spectra suggests that there is some differencein the profile of skin metabolites between the individuals in thisstudy. Such differences are not surprising given the inherentdifferences in metabolic activity, physical activity, and life style. Inthis specific study, lactic acid levels are found to vary betweenindividuals.

Example 4 Measuring Metabolites of Samples to Assess State of Skin

Skin metabonomics is able to discriminate metabolite profile variationand is very useful for evaluating skin biochemical states, optimizingclinical study design and interpreting clinical results. There are manyfactors responsible for skin changes. Factors such as bowel movement(BM), urine, pH and temperature are among these factors and may impactskin simultaneously in a diapered environment. Typical examples are legcrease areas that are typically occluded while lower buttock areasexperience more insults from repeated BM and urine soiling and frequentover-hydration. NMR is able to detect the combined impact of thesefactors on skin and differentiate these skin states. Skin treatmentefficacy such as wipe cleaning can also be evaluated and documented byNMR analysis.

The predictive aspect of the models of the above testing samples may beexamined in the following experiment.

The NMR data from skin samples, such as those from Example 1, may beprocessed using Partial Least Squares-Discriminant Analysis (PLS-DA)(SIMCA-P+ from Umetrics) and the scores plot is shown in FIG. 6. Theskin sample numbers shown in FIG. 6 may be identified as follows inTable 1.

TABLE 1 Skin Sample Number Skin Sample Code 2 24 hr full occlusion(2_24_f) 3 24 hr full occlusion (3_24_f) 5 72 hr full occlusion (5_72_f)6 Control (6_72_c) 10 72 hr full occlusion (10_72_f) 11 Control(11_72_c) 13 24 hr full occlusion (13_24_f) 16 72 hr full occlusion(16_72_f) 17 72 hr full occlusion (17_72_f) 18 Control (18_72_c) 19 72hr full occlusion (19_72_f) 20 Control (20_72_c) 23 Control (23_72_c) 2472 hr full occlusion (24_72_f)

Referring to FIG. 6, the occluded skin samples and those from thenon-occluded skin are separated into disparate groups. Some of themetabolites are highly elevated, such as UCA, in occluded skin. Theextent of occlusion can be assessed based on the level of similarity tountreated (Class 1) or full occluded skin (Class 2) as predicted byPLS-DA calculation. The training set used to generate FIG. 6, may definea model to predict skin classification of partially occluded skinsamples.

Partial occlusions may be a useful model for that of diapered skin andcould be responsible for inducing skin irritation like that in legcreases or may not cause any visible redness as in upper buttock area.The skin responses can be a Class 1-like the control, Class 2-like fullocclusion, or something in between. The values in Table 2 (in sample ID:f—full occlusion, p—partial occlusion and c—control) may demonstrate apossibility of predicting the extent of partial occlusion on diaperedand other types of skin environments. The prediction is demonstrated inTable 2 below. “Similarity to Control” score close to 1 in the analysisis classified as control (Class 1) and “Similarity to Full Occlusion”score close to 1 in the analysis is classified as full occlusion (Class2). Almost all of the full occlusion and control skin can be predictedcorrectly by the prediction calculation. The similarity value of thetreated skin to Class 1 or Class 2 offers a measure to rank order skinfor its response to the treatment or product use.

TABLE 2 PLS- Similarity Sample DA Similarity to Full Code SubjectTreatment Class to Control Occlusion  1_24_p A 24 hr partial occl.0.82413 0.17586  2_24_f 24 hr full occl. 2 0.04026 0.95973  3_24_c 24 hrcontrol 1 1.00391 −0.0039 10_72_f B 72 hr full occl. 2 0.0733 0.9266911_72_c 72 hr control 1 0.97606 0.02393 12_72_p 72 hr partial occl.0.6589 0.34109 19_72_f C 72 hr full occl 2 −0.029 1.02908 20_72_c 72 hrcontrol 1 0.96491 0.03508 21_72_p 72 hr partial occl. 0.19195 0.80804

Example 5 Metabolites at Various Skin Sites

Baby skin in a diaper environment may be slightly different due to itslocal environment. For example, skin in leg creases is typicallyoccluded whereas that in the lower buttock area is over-hydrated andexperiences more abrasions. Metabonomics has the power to distinguishthe differences.

Ten skin samples from both opposite sites of a baby's behind (left vs.right side) are collected, two from each type of skin site (FIG. 7A):intertriginous region (leg crease area) (3 & 4 in FIG. 7A), front thigh(1 & 2 in FIG. 7A), lower buttock (7 & 8 in FIG. 7A), upper buttock (5 &6 in FIG. 7A), and back thigh (9 & 10 in FIG. 7A). The samples areprepared as follows. The samples are extracted by placing a single skinstrip in a glass vial, adding 1 mL water and sonicating the vial for 30min. The sample extracts from both opposite sites are combined and aredried under nitrogen (N₂) and are reconstituted in 200 μL of 200 mMsodium phosphate containing 0.1% (w/v) sodium azide in D₂O at pH 7.4.The samples are transferred to sample tubes for ¹H NMR analysis. The ¹HNMR data are collected on a Bruker Avance 600 MHz instrument runningXWINNMR (as available in the Bruker Avance software system) and equippedwith a 2.5 mm Broad Band Inverse (BBI) probe. A presat pulse program isused for water suppression. A total number of 2048 scans are collected.

The proton resonances from these samples are similar in the aliphaticregion (FIG. 7B). However, in the aromatic region (FIG. 7C), UCA signalsare stronger in skin samples from intertriginous regions than those fromthe back thigh. The UCA resonances from other sites are weaker (6.4,7.37, 7.41 and 7.88 ppm).

This result may be consistent with previous skin studies wherein levelsof UCA increased upon skin occlusion. The intertriginous (leg crease)area is constantly under abrasive and non-breathable conditions,therefore the region is more occluded than other areas. Lack of UCAsignal from the lower buttock region could be due to the overhydrationand rewetting during diaper wearing. Skin chafing might be the majorcause of the elevated UCA levels in the back thighs. Lack of cis-UCAsignals in the baby leg crease skin sample may be explained as lack ofsun light exposure, and therefore, no photochemical isomerization fromtrans-UCA to cis-UCA.

Example 6 Measuring Occlusion and Wipes Cleaning Effects

The effects of various skin treatments are assessed by measuring themetabolites from a series of subjects. A set of skin samples from fiveindividuals are analyzed wherein each individual receives six differenttreatments: Dry control (no treatment); Dry 1 swipe (wipe one time onnon-treated skin); Dry 5 swipes (wipe five times on non-treated skin);Wet control (occlusion); Wet 1 swipe (wipe one time on occluded skin);Wet 5 swipes (wipe five times on occluded skin). Tape stripping tocollect the skin sample is performed after each treatment. The samplesare extracted by placing a single skin strip in a glass vial, adding 1mL water and sonicating the vial for 30 min and vortexing the samplesfor 30 seconds. The sample extracts are dried under nitrogen (N₂) andare reconstituted in 200 μL of 200 mM sodium phosphate containing 0.1%(w/v) sodium azide in D₂O at pH 7.4. TSP (3-trimethylsilyl-1-propanesulfonic acid sodium salt) may be added. The samples are transferred to3 mm sample tubes for ¹H NMR analysis.

Each sample is manually tuned and shimmed. A presat pulse sequence isused to suppress water signal. Key parameters include nt=2048; at =3.277s; delay=1.5 s; mixing time=0.1 s; and temp=25° C. A total of 30 spectraare used to generate original data matrix. The data matrix is thenanalyzed using nmrPro in MATLAB (by Mathworks) environment. The datamatrix is pretreated to remove solvent and internal reference peaks, allintegrals are scaled to constant value for fair comparison, and noisebaseline is cut off by setting appropriate threshold. The preprocessingensures that factors generated from Molecular Factor Analysis (MFA)(Eads, C., Analytical Chem. 76(7):1982-1990 (2004)) are only subject tostatistically significant variations. Molecular factor analysis isperformed to extract intrinsic grouping potential of the treatments andchemical information about the marker molecules.

The NMR spectra between individuals may vary, which is not unexpectedconsidering that skin extracts would be expected to differ dependingupon each individual's skin type and environment. Upon occlusion (24hrs, wet treatment), aromatic signals in proton NMR spectrum mayincrease. The signal intensity may change suggesting metabolic variationduring occlusion. The effect that wiping has on skin samples collectedis that the amount of extracts found in the skin sample may decreasesignificantly, for each individual. This result may indicate that wipingremoves metabolites from the skin surface and brings skin back to afresh state. FIG. 8 shows an illustration of, from top to bottom, theNMR spectra from a single subject with occluded skin, occluded skinafter one swipe with a zinc oxide (ZnO) wipe (1% ZnO in aqueous solutioncontaining 2% parabens preservative system), and occluded skin after 5swipes with a ZnO wipe. The spectrum of the skin wiped five times(bottom trace) shows almost no extractable metabolites, indicating thatthe five wipes removed most if not all metabolites from occluded skin.

The NMR data is then processed using MFA and a scores plot on threefactors is generated (FIG. 9). The plot shows population separation ofdry skin samples from wet skin samples. The Dry control group isclustered away from wet control group. After five wipes, Dry 5 swipe andWet 5 swipe groups shift to the left of the map, and become morescattered. This is more clearly demonstrated in FIG. 10 for the skinsamples from one subject where occlusion may shift skin to a higherscore on Factor 3 while wipe cleaning may shift skin to a higher scoreon Factor 6. The impact of wiping treatment on the skin may be similarregardless of skin pre-treatment (i.e., dry vs. wet). Each factorgenerated from data analysis may represent either a single metabolite ora combination of multiple marker molecules. The fact that the threefactors from MFA may permit differentiation of the untreated (dry) skinfrom the occluded (wet) skin may indicate metabolic activity differencesamong skin samples under different treatments.

Occlusion may shift biomarker presentation away from the controlbiomarker expression pattern. Wiping with lotioned wipes may remove muchof the skin metabolites and put both control and occluded skin in asimilar normal condition.

Example 7 Assessment and Comparison of Various Treatments

Wiping skin with lotion wipes removes soils from skin surface, leavingskin clean and fresh. A study, in which skin is repeatedly occluded andsubsequently wiped cleaned for three days, shows a clear separation ofthis lotion wipes treated skin from a water and preservative vehicle.

The efficacy of several skin treatments are assessed among a variety ofsubjects. Samples from 14 subjects are collected. Briefly, the forearmof each subject is occluded for 1 day and then receives one of the 6treatments listed below. The occlusion is done according to the methodas described above. After treatment, the site is occluded again with afresh occlusion patch. The occlusion/wipe treatment is repeated for atotal of 3 days. On the 4th day each site is tape stripped. A total of84 samples are analyzed—six from each subject in the followingcategories: occluded control (A); 1.0% ZnO wipe (B); 1.0% ZnO lotion (5μL/cm²) (C); 1.0% ZnO lotion (1 μL/cm²) (D); water with preservativewipe (E); water with preservative expressed from wipes (5 μL/cm²) (F).Each sample is analyzed using presat pulse sequence for watersuppression. The spectra are then transformed into ASCII format andprocessed using MFA.

¹H NMR spectra of the skin samples with different treatments are foundto be different in both the aromatic and aliphatic regions. In general,the intensities of most resonances are significantly reduced in the ZnOlotion wipe treated sites. FIG. 11 shows the overlay of the aromaticregions of the ¹H NMR spectra from a single subject. The proton signalintensity may decrease with ZnO lotion wiping treatment (FIG. 11,spectrum B), compared with those of occluded control (FIG. 11, spectrumA) and water/preservative wipes (FIG. 11, spectrum C). These results mayindicate that the ZnO wipes are efficient in removing these metabolitesfrom skin, while water/preservative wipes appear to be less efficient.

MFA results are shown in FIG. 12 as a scores plot of two factors. Theplot shows a separation of ZnO lotion wipes treatment from both occludedcontrol and water/preservative wipes.

Individual variation is observed in FIG. 12 and results for subject #115appear to be an outlier. In examining the loading of Factor 1, lacticacid, one of the major metabolites that is often used as a marker forsweat gland activity, is identified as responsible for Subject #115'sshift away from the rest of the subjects. This may become clearer bydisplaying the scores plot in a 3D format (FIG. 13A) using an additionalfactor. It is not surprising to find Subject #115 positioned separatelyfrom the rest of the group in a 2D plot of Factor 1, with a high scoreon Factor 1.

As indicated in FIG. 13B, which represents a high resolution expansionof FIG. 13A, a skin response to ZnO wipes may be separated from occludedskin and vehicle (water/preservative only) wipes. The separation betweenoccluded skin and ZnO wipe treated skin may suggest unique biomarkerpresentation on the subject's skin as the result of the occlusion andwipes treatments. Individual variations in skin may be recognized as amajor cause for poor clinical results based on conventional methods suchas visual grading and TEWL. However, results from the skin metabonomicsshow that the skin conditions caused by either individual variations orskin treatments can be resolved, even when the individual variation isfar greater than skin responses to the treatments.

Results from other skin treatments (C, D and F, defined above) may showsimilar responses as their wipes counterparts. Skin samples treated withZnO lotion wipes are distinguishable from both vehicle(water/preservative) wipes and occlusion control. The ZnO lotion wipecleaning effect can be seen by overlaying individual ¹H NMR spectra fromdifferent treatments through multivariate data analysis. The grouping ofskin samples with each treatment after the multivariate analysis mayindicate metabolic differences. The differences are not caused by lotionresidues.

The foregoing describes and exemplifies the invention but is notintended to limit the invention defined by the claims which follow. Allof the arrays and/or methods disclosed and claimed herein can be madeand executed without undue experimentation in light of the presentdisclosure. While the materials and methods of this invention have beendescribed in terms of specific embodiments, it will be apparent to thoseof skill in the art that variations may be applied to the materialsand/or methods and in the steps or in the sequence of steps of themethods described herein without departing from the concept, spirit andscope of the invention. More specifically, it will be apparent thatcertain agents which are both chemically and physiologically related maybe substituted for the agents described herein while the same or similarresults would be achieved. All such similar substitutes andmodifications apparent to those of ordinary skill in the art are deemedto be within the spirit, scope and concept of the invention as definedby the appended claims.

All documents cited in the Detailed Description of the Invention are, inrelevant part, incorporated herein by reference; the citation of anydocument is not to be construed as an admission that it is prior artwith respect to the present invention. To the extent that any meaning ordefinition of a term in this written document conflict with any meaningor definition of the term in a document incorporated by reference, themeaning or definition assigned to the term in this written documentshall govern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm”.

1. A method to assess skin condition, the method comprising: extractingbiomarkers from a human skin sample collected using a non-invasivesampling technique; obtaining a spectrograph for the biomarkers;analyzing the spectrograph using a pattern recognition technique; andevaluating the pattern recognition technique results for patternsconsistent with a skin state.
 2. The method of claim 1, furthercomprising collecting a human skin sample using a non-invasive samplingtechnique.
 3. The method of claim 2, wherein the non-invasive samplingtechnique is selected from the group consisting of mechanical scraping,swabbing, direct elution, pressure blotting, electric transfer, andtape-stripping.
 4. The method of claim 1, wherein the spectrograph isobtained by a method selected from the group consisting of highresolution NMR, mass spectrometry, capillary electrophoresis, liquidchromatography, and combinations thereof.
 5. The method of claim 1,wherein the spectrograph is obtained by liquid chromatography-massspectrometry.
 6. The method of claim 1, wherein the skin state isselected from the group consisting of occluded, clean with regard tourine, clean with regard to bowel movement, and recently cleaned with awipe.
 7. The method of claim 1, wherein the pattern recognitiontechnique is unsupervised.
 8. The method of claim 7, wherein the patternrecognition technique is selected from the group consisting of principalcomponents analysis, hierarchical cluster analysis, and non-linearmapping.
 9. The method of claim 1, wherein the pattern recognitiontechnique is supervised.
 10. The method of claim 9, wherein the patternrecognition technique is selected from the group consisting of partialleast squares discriminant analysis and molecular factor analysis.
 11. Amethod for establishing biomarker patterns consistent with a given skinstate, the method comprising the steps of: extracting a first set ofbiomarkers from a human skin sample collected using a non-invasivesampling technique and having a first known skin state; obtaining afirst spectrograph for the first set of biomarkers; analyzing the firstspectrograph using a pattern recognition technique; extracting a secondset of biomarkers from a human skin sample collected using anon-invasive sampling technique and having a second known skin state;obtaining a second spectrograph for the second set of biomarkers;analyzing the second spectrograph using a pattern recognition technique;and comparing the pattern recognition technique results for the firstand second sets of biomarkers to identify pattern differences associatedwith one or more known skin states.
 12. The method of claim 11, whereinthe first skin state and the second skin state are independentlyselected from the group consisting of occluded, clean with regard tourine, clean with regard to bowel movement, and recently cleaned with awipe.
 13. The method of claim 11 further comprising: extracting a thirdset of biomarkers from a human skin sample having an unknown skin statecollected using a non-invasive sampling technique; obtaining a thirdspectrograph for the third set of biomarkers; analyzing the thirdspectrograph using a pattern recognition technique; and comparing thepattern recognition technique results for the first, second, and thirdsets of biomarkers to identify pattern differences associated with oneor more known skin states.
 14. The method of claim 11, wherein thenon-invasive sampling technique is selected from the group consisting ofmechanical scraping, swabbing, direct elution, pressure blotting,electric transfer, and tape-stripping.
 15. The method of claim 11,wherein the spectrograph is obtained by a method selected from the groupconsisting of high resolution NMR, mass spectrometry, capillaryelectrophoresis, liquid chromatography, and combinations thereof. 16.The method of claim 11, wherein the spectrograph is obtained by liquidchromatography-mass spectrometry.
 17. The method of claim 11, whereinthe skin state is selected from the group consisting of occluded, cleanwith regard to urine, clean with regard to bowel movement, and recentlycleaned with a wipe.
 18. The method of claim 11, wherein the first andsecond skin samples are taken from a single human subject at differentpoints in time.
 19. The method of claim 11, wherein the first and secondskin samples are taken from a single human subject at skin sitessubjected to different treatments prior to collecting the first andsecond skin samples.